ArticleJMIR nursing2026
Insights Into Factors Affecting Nurses' Knowledge of and Attitudes Toward AI and Implications for Successful AI Integration in Critical Care: Cross-Sectional Study.
Article in JMIR nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Factors Influencing Nursing Internship Students' Readiness to Use AI: Cross-Sectional Study Using Neural Network Analysis.JMIR nursing · 2026Article
- Article
- The influence of AI literacy on fear of negative evaluation among students and researchers: a multisite cross-sectional study.BMC medical education · 2026Article
- Knowledge, Attitude, Benefits, Risks, Barriers, Professional Impact, and Preparedness of Nursing Students Toward the Utilization of Artificial Intelligence in Healthcare.Nursing reports (Pavia, Italy) · 2026Article
- Application of the Technology Acceptance Model to Predict Nursing Students' Intention to Use Informatics: Cross-Sectional Study.JMIR nursing · 2026Article
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6 authors.
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Abstract
Background: Assessing the current landscape of nurses' knowledge and attitudes is a critical first step in facilitating a smooth and effective transition toward artificial intelligence (AI)-enhanced critical care. Objective: This study aimed to assess the levels of and factors affecting the knowledge of and general attitudes toward AI in critical care among nurses. Methods: A cross-sectional correlational design was used with 203 critical care nurses in Hail, Saudi Arabia, using the Nurses' AI Knowledge Questionnaire and the 20-item General Attitudes Toward Artificial Intelligence Scale from May 2025 to July 2025. Data were analyzed using 2-tailed t tests, ANOVA, Pearson correlation, and multivariable linear regression. Statistical significance was set at P<.05. Results: Critical care nurses demonstrated moderate knowledge of (mean score 4.93, SD 1.78) and positive attitudes toward AI (mean score 64.39, SD 8.26). A moderate positive correlation was found between knowledge of and attitudes toward AI (r=0.45; P<.001). In multivariable analyses, older age was associated with lower knowledge (≥40 years: β=-1.29, 95% CI -2.12 to -0.45; P=.003) and less positive attitudes (β=-8.97, 95% CI -12.49 to -5.44; P<.001). Female nurses reported lower knowledge (β=-0.69, 95% CI -1.20 to -0.19; P=.007) and less positive attitudes (β=-2.65, 95% CI -4.78 to -0.52; P=.02) than male nurses. Greater experience (>5 years) was positively associated with knowledge (β=1.20, 95% CI 0.65-1.75; P<.001) and attitudes (β=8.08, 95% CI 5.76-10.41; P<.001). Conclusions: Critical care nurses in Hail demonstrated moderate knowledge of and positive attitudes toward AI, which varied based on their demographic and professional characteristics. These findings highlight the need to strengthen AI literacy and provide targeted support to groups with lower scores, which may enhance readiness for AI integration in critical care settings.
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